---
title: Determinants of Bank Performance
url: https://www.ml-quant.com/papers/repec/bco-ncafaa-v-9-y-2023-p-26-41/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:bco:ncafaa::v:9:y:2023:p:26-41
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fapi.eurokd.com%2FUploads%2FArticle%2F704%2FNCAF.2023.09.03.pdf%3Bh%3Drepec%3Abco%3Ancafaa%3A%3Av%3A9%3Ay%3A2023%3Ap%3A26-41
featured: 2025-03-05
citations: unknown
topic: Risk, Credit & Banking
---


# Determinants of Bank Performance

The paper suggests new research areas in understanding banks' performance, focusing on digital transformation, AI, and the effects of COVID-19.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fapi.eurokd.com%2FUploads%2FArticle%2F704%2FNCAF.2023.09.03.pdf%3Bh%3Drepec%3Abco%3Ancafaa%3A%3Av%3A9%3Ay%3A2023%3Ap%3A26-41
- Identifier: RePEc:bco:ncafaa::v:9:y:2023:p:26-41
- Released: 2023-02-19
- First featured: Quant Letter No. 87 (2025-03-05): https://www.ml-quant.com/issues/2025-03-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

## Related

- [The Effect of Financial Risks on the Financial Performance of Banks listed on Bahrain Bourse: An Empirical Stud](https://www.ml-quant.com/papers/ssrn/5147102/): Capital risks significantly impact the financial performance of banks listed on the Bahrain Bourse.
- [Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework](https://www.ml-quant.com/papers/arxiv/2502.14479/): A study comparing three loan behavior modeling techniques finds multinomial logistic regression to be the most effective, potentially improving loss reserve estimates in banking.
- [A cost of capital approach to determining the LGD discount rate](https://www.ml-quant.com/papers/arxiv/2503.23992/): The paper introduces a new method for estimating banking losses, using a market-consistent price for defaulted loans.
- [Predicting Bank Distress in Europe: Using Machine Learning and a Novel Definition of Distress](https://www.ml-quant.com/papers/ssrn/5098026/): The paper presents a machine learning-based early warning system to predict distress in large European banks, with the random forest model performing best.
- [Use of AI and ML in Banking & Finance to Improve Decision-Making, Automate Processes, and Enhance Customer Experiences](https://www.ml-quant.com/papers/ssrn/5086625/): The manuscript discusses the potential of AI and ML in finance and regulatory compliance, but also points out challenges related to data privacy, algorithmic bias, and model explainability.
- [Machine and Deep Learning for Credit Scoring: A compliant approach](https://www.ml-quant.com/papers/arxiv/2412.20225/): The research proposes new BASEL 2 and 3 compliant techniques for credit scoring in banks, demonstrating improved performance and default capture rate with Gradient Boosting Machines.